mmdetection-2.26.0/configs/common/lsj_100e_coco_instance.py

_base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
 mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
image_size = (1024, 1024)

file_client_args = dict(backend='disk')
# comment out the code below to use different file client
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))

train_pipeline = [
 dict(type='LoadImageFromFile', file_client_args=file_client_args),
 dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
 dict(
 type='Resize',
 img_scale=image_size,
 ratio_range=(0.1, 2.0),
 multiscale_mode='range',
 keep_ratio=True),
 dict(
 type='RandomCrop',
 crop_type='absolute_range',
 crop_size=image_size,
 recompute_bbox=True,
 allow_negative_crop=True),
 dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)),
 dict(type='RandomFlip', flip_ratio=0.5),
 dict(type='Normalize', **img_norm_cfg),
 dict(type='Pad', size=image_size), # padding to image_size leads 0.5+ mAP
 dict(type='DefaultFormatBundle'),
 dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
 dict(type='LoadImageFromFile', file_client_args=file_client_args),
 dict(
 type='MultiScaleFlipAug',
 img_scale=(1333, 800),
 flip=False,
 transforms=[
 dict(type='Resize', keep_ratio=True),
 dict(type='RandomFlip'),
 dict(type='Normalize', **img_norm_cfg),
 dict(type='Pad', size_divisor=32),
 dict(type='ImageToTensor', keys=['img']),
 dict(type='Collect', keys=['img']),
 ])
]

# Use RepeatDataset to speed up training
data = dict(
 samples_per_gpu=2,
 workers_per_gpu=2,
 train=dict(
 type='RepeatDataset',
 times=4, # simply change this from 2 to 16 for 50e - 400e training.
 dataset=dict(
 type=dataset_type,
 ann_file=data_root + 'annotations/instances_train2017.json',
 img_prefix=data_root + 'train2017/',
 pipeline=train_pipeline)),
 val=dict(
 type=dataset_type,
 ann_file=data_root + 'annotations/instances_val2017.json',
 img_prefix=data_root + 'val2017/',
 pipeline=test_pipeline),
 test=dict(
 type=dataset_type,
 ann_file=data_root + 'annotations/instances_val2017.json',
 img_prefix=data_root + 'val2017/',
 pipeline=test_pipeline))
evaluation = dict(interval=5, metric=['bbox', 'segm'])

# optimizer assumes bs=64
optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.00004)
optimizer_config = dict(grad_clip=None)

lr_config = dict(
 policy='step',
 warmup='linear',
 warmup_iters=500,
 warmup_ratio=0.067,
 step=[22, 24])
runner = dict(type='EpochBasedRunner', max_epochs=25)